Abstract
Traditionally, mental health specialists monitor their patients’ social behavior by applying subjective self-report questionnaires in face-to-face meetings. Usually, the application of the self-report questionnaire is limited by cognitive biases (e.g., memory bias and social desirability). As an alternative, we present a solution to detect context-aware sociability patterns and behavioral changes based on social situations inferred from ubiquitous device data. This solution does not focus on the diagnosis of mental states, but works on identifying situations of interest to specialized professionals. The proposed solution consists of an algorithm based on frequent pattern mining and complex event processing to detect periods of the day in which the individual usually socializes. Social routine recognition is performed under different context conditions to differentiate abnormal social behaviors from the variation of usual social habits. The proposed solution also can detect abnormal behavior and routine changes. This solution uses fuzzy logic to model the knowledge of the mental health specialist necessary to identify the occurrence of behavioral change. Evaluation results show that the prediction performance of the identified context-aware sociability patterns has strong positive relation (Pearson’s correlation coefficient >70%) with individuals’ social routine. Finally, the evaluation conducted recognized that the proposed solution leading to the identification of abnormal social behaviors and social routine changes consistently.
Highlights
Mental health refers to the psychological, social, and emotional well-being, so influencing our behaviors, feelings, and thoughts
This paper has the following contributions: (i) we present an update of the formalization of the algorithm to detect context-aware sociability patterns; (ii) we introduce a solution for recognizing abnormal social behaviors and social routine changes; (iii) we use fuzzy logic to model knowledge of the mental health specialist needed to recognize social behavior changes; (iv) we evaluate the ability of the sociability patterns identified by the proposed solution to explain and predict users’ social behaviors; and (v) we present an extensive analysis to evaluate the social behavior change detection solution
We identified that the prediction performance of social patterns has a strong positive correlation with the stability of the social routine (i.e., Pearson correlation coefficient greater than +0.7) in all Context Attributes (CAs) considered, so enabling to recognize that the proposed solution detects patterns consistent with the social behaviors of the monitored individuals
Summary
Mental health refers to the psychological, social, and emotional well-being, so influencing our behaviors, feelings, and thoughts. Mental disorder is a term used to describe mental health problems, such as depression, schizophrenia, and social anxiety. These disorders are responsible for affecting aspects such as mood, sleep, personality, thoughts, and social relationships [2]. Mental disorders are a health problem prevalent in a large part of the world population, affecting about 700 million people worldwide [3]. Depression is a mental disorder that affects more than 300 million people worldwide, while around 800,000 people commit suicide each year [4]. It is possible to recognize that the prevalence of mental health problems has reached a significant part of the world population
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